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wrapr: for sweet R code

This article is on writing sweet R code using the wrapr package.

The problem

Consider the following R puzzle. You are given: a data.frame, the name of a column that you wish to find missing values (NA) in, and the name of a column to land the result. For instance:

d <- data.frame(x = c(1, NA))
print(d)
##    x
## 1  1
## 2 NA

cname <- 'x'
print(cname)
## [1] "x"

rname <- paste(cname, 'isNA', sep = '_')
print(rname)
## [1] "x_isNA"

How do you write generic code to populate the column x_isNA with which rows of x are missing?

The “base R” solution

In “base R” (R without additional packages) this is easy.

When you know the column names while writing the code:

d2 <- d
d2$x_isNA <- is.na(d2$x)

print(d2)
##    x x_isNA
## 1  1  FALSE
## 2 NA   TRUE

And when you don’t know the column names while writing the code (but know they will arrive in variables later):

d2 <- d
d2[[rname]] <- is.na(d2[[cname]])

The “base R” solution really is quite elegant.

The “all in” non-standard evaluation dplyr::mutate solution (pre-rlang)

As far as I can tell the “all in” non-standard evaluation dplyr::mutate solution is something like the following.

When you know the column names while writing the code:

library("dplyr")
## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

d %>% mutate(x_isNA = is.na(x))
##    x x_isNA
## 1  1  FALSE
## 2 NA   TRUE

And when you don’t know the column names while writing the code (but know they will arrive in variables later):

d %>%
  mutate_(.dots =
            stats::setNames(list(lazyeval::interp(
              ~ is.na(VAR),
              VAR = as.name(cname)
            )),
            rname))
## Warning: mutate_() is deprecated. 
## Please use mutate() instead
## 
## The 'programming' vignette or the tidyeval book can help you
## to program with mutate() : https://tidyeval.tidyverse.org
## This warning is displayed once per session.

##    x x_isNA
## 1  1  FALSE
## 2 NA   TRUE

The “all in” non-standard evaluation dplyr::mutate solution (rlang)

rlang obsoleted the above and now suggests code such as the following:

d %>%
  mutate(!!rlang::sym(rname) := is.na(!!rlang::sym(cname)))
##    x x_isNA
## 1  1  FALSE
## 2 NA   TRUE

Note “the obvious” rlang solution does not work:

d %>%
  mutate(!!rname := is.na(!!cname))
##    x x_isNA
## 1  1  FALSE
## 2 NA  FALSE

(though the above sort of notation is allowed “in select contexts”.)

The sweet wrapr::let dplyr::mutate solution

We will only work the harder “when you don’t yet know the column name” (or parametric) version:

library("wrapr")
## 
## Attaching package: 'wrapr'

## The following object is masked from 'package:dplyr':
## 
##     coalesce

let(list(CNAME = cname, RNAME = rname),
    d %>% mutate(RNAME = is.na(CNAME))
)
##    x x_isNA
## 1  1  FALSE
## 2 NA   TRUE

I think that this is pretty sweet, and can really level up your dplyr game.

wrapr::let is available from CRAN and already has a number of satisfied users:

If function behavior depends on variable names, then convenient control of functions is eventually going to require convenient control of variable names; so needing to re-map variable names at some point is inevitable.